Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Faster Training Algorithms for Structured Sparsity-Inducing Norm
Authors: Bin Gu, Xingwang Ju, Xiang Li, Guansheng Zheng
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our algorithm is much more efficient than the network-flow algorithm, while retaining the similar generalization performance. |
| Researcher Affiliation | Academia | School of Computer & Software, Nanjing University of Information Science & Technology, P.R.China Department of Electrical & Computer Engineering, University of Pittsburgh, USA Computer Science Department, University of Western Ontario, Canada |
| Pseudocode | Yes | Algorithm 1 Inexact Subgradient Descent Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | The Nartual, Tesdata, Yearst, Sector and Realsim datasets are from http://www.mldata.org/ repository/data/. The Coil20 dataset is from http: //www.cs.columbia.edu/CAVE/software/. The Movielen100k dataset is from http://archive.ics. uci.edu/ml/datasets.html. |
| Dataset Splits | No | The paper lists datasets used but does not provide specific train/validation/test dataset splits or their percentages/counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or processing units used for running the experiments. |
| Software Dependencies | No | The paper states that the algorithms are implemented in 'MATLAB' but does not provide a specific version number or other software dependencies with their versions. |
| Experiment Setup | Yes | In experiments, the outer loop iteration is selected from {300, 500, 1000} to guarantee convergence. The value of stepsize γ is selected from 10 3, 10 4, 10 5, 10 6 to satisfy γ 1 L. The λ is set 0.1. The initial vector xi has 20% percent nonzero components, randomly selected, and uniformly generated between [ 1, 1] for normalization. The weight ηg for each group is also randomly generated between [0, 1]. |